Online on-board optimization of cutting parameter for energy efficient CNC milling

Energy efficiency is one of the main drivers for achieving sustainable manufacturing. Advances in machine tool design have reduced the energy consumption of such equipment, but still machine tools remain one of the most energy demanding equipment in a workshop. This study presents a novel approach aimed to improve the energy efficiency of machine tools through the online optimization of cutting conditions. The study is based on an industrial CNC controller with smart algorithms optimizing the cutting parameters to reduce the overall machining time while at the same time minimizing the peak energy consumption

Addressing real-time control problems in complex environments using dynamic multi-objective evolutionary approaches

The demand for increased automation of industrial processes generates control problems that are dynamic, multi-objective and noisy at the same time. The primary hypothesis underlying this research is that dynamic evolutionary methods could be used to address dynamic control problems where con icting control criteria are necessary. The aim of this research is to develop a framework for on-line optimisation of dynamic problems that is capable of a) representing problems in a quantitative way, b) identifying optimal solutions using multi-objective evolutionary algorithms, and c) automatically selecting an optimal solution among alternatives. A literature review identi es key problems in the area of dynamic multi-objective optimisation, discusses the on-line decision making aspect, analyses existing Multi- Objective Evolutionary Algorithms (MOEA) applications and identi es research gap. Dynamic evolutionary multi-objective search and on-line a posteriori decision maker are integrated into an evolutionary multi-objective controller that uses an internal process model to evaluate the tness of solutions. Using a benchmark multi-objective optimisation problem, the MOEA ability to track the moving optima is examined with di erent parameter values, namely, length of pre-execution, frequency of change, length of prediction interval and static mutation rate. A dynamic MOEA with restricted elitism is suggested for noisy environments.To address the on-line decision making aspect of the dynamic multi-objective optimisation, a novel method for constructing game trees for real-valued multiobjective problems is presented. A novel decision making algorithm based on game trees is proposed along with a baseline random decision maker. The proposed evolutionary multi-objective controller is systematically analysed using an inverted pendulum problem and its performance is compared to Proportional{ Integral{Derivative (PID) and nonlinear Model Predictive Control (MPC) approaches. Finally, the proposed control approach is integrated into a multi-agent framework for coordinated control of multiple entities and validated using a case study of a tra c scheduling problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Technoeconomic distribution network planning using smart grid techniques with evolutionary self-healing network states

The transition to a secure low-carbon system is raising a set of uncertainties when planning the path to a reliable decarbonised supply. The electricity sector is committing large investments in the transmission and distribution sector upon 2050 in order to ensure grid resilience. The cost and limited flexibility of traditional approaches to 11 kV network reinforcement threaten to constrain the uptake of low-carbon technologies. This paper investigates the suitability and cost-effectiveness of smart grid techniques along with traditional reinforcements for the 11 kV electricity distribution network, in order to analyse expected investments up to 2050 under different DECC demand scenarios. The evaluation of asset planning is based on an area of study in Milton Keynes (East Midlands, United Kingdom), being composed of six 11 kV primaries. To undertake this, the analysis used a revolutionary new model tool for electricity distribution network planning, called scenario investment model (SIM). Comprehensive comparisons of short- and long-term evolutionary investment planning strategies are presented. The work helps electricity network operators to visualise and design operational planning investments providing bottom-up decision support

Addressing real-time control problems in complex environments using dynamic multi-objective evolutionary approaches

The demand for increased automation of industrial processes generates control problems that are dynamic, multi-objective and noisy at the same time. The primary hypothesis underlying this research is that dynamic evolutionary methods could be used to address dynamic control problems where con icting control criteria are necessary. The aim of this research is to develop a framework for on-line optimisation of dynamic problems that is capable of a) representing problems in a quantitative way, b) identifying optimal solutions using multi-objective evolutionary algorithms, and c) automatically selecting an optimal solution among alternatives. A literature review identi es key problems in the area of dynamic multi-objective optimisation, discusses the on-line decision making aspect, analyses existing Multi- Objective Evolutionary Algorithms (MOEA) applications and identi es research gap. Dynamic evolutionary multi-objective search and on-line a posteriori decision maker are integrated into an evolutionary multi-objective controller that uses an internal process model to evaluate the tness of solutions. Using a benchmark multi-objective optimisation problem, the MOEA ability to track the moving optima is examined with di erent parameter values, namely, length of pre-execution, frequency of change, length of prediction interval and static mutation rate. A dynamic MOEA with restricted elitism is suggested for noisy environments.To address the on-line decision making aspect of the dynamic multi-objective optimisation, a novel method for constructing game trees for real-valued multiobjective problems is presented. A novel decision making algorithm based on game trees is proposed along with a baseline random decision maker. The proposed evolutionary multi-objective controller is systematically analysed using an inverted pendulum problem and its performance is compared to Proportional{ Integral{Derivative (PID) and nonlinear Model Predictive Control (MPC) approaches. Finally, the proposed control approach is integrated into a multi-agent framework for coordinated control of multiple entities and validated using a case study of a tra c scheduling problem

Computer aided design of involute gear shaper cutters

The global market competition has put a pressure on the industry to reduce lead time and increase quality of parts. In particular, the area of gear cutting tool design requires capability to rapidly produce tool designs with increased tool geometry precision. The aim of this research is to explore the field of gear shaper cutter design and produce a model and software that would enable creation of more precise cutter designs, faster. A literature survey of involute cylindrical gearing geometry highlights deficiencies in addressing industry requirements of tool manufacturing, as most of the research in the area is focused on problems of gear design, but not tool design. This research, therefore, focuses on the process of gear manufacturing using the gear shaper cutters. It attempts to develop an analytical model and a set of tools that are able to aid the design process of gear shaper cutter. With cylindrical gear being essentially " at" and therefore easier to express an- alytically, the focus of recent papers has shifted to non-cylindrical and non-involute gear trains. Yet the geometry of the tools used to produce cylindrical gears remains largely untapped area, in particular the geometry of gear shaper cutters. The knowl- edge about the profile of the cutter teeth and the profile cut by the cutter in the gear blank are essential for high precision gear manufacturing. The gear shaper cutter is a metal cutting tool and therefore should be re-sharpened on a regular basis. These re-sharpening operations change the profile of the tool and, consequently, the profile of the gear it cuts. The changes in the cutter and gear geometry are analysed in this thesis and a way to calculate the life span of the tool is suggested. Gear shaper cutter profiles usually have one or more modifications to the theoret- ical ideal involute profile applied. Some of these modifications are used to produce a better cutter profile, while other should in turn produce modifications to the gear profile, and, finally, some are used to overcome generated gear profile constraints. Computer-Aided Design (CAD) software for gear shaper cutter design should be able to show the profile of the cutter and the profile cut by the cutter with arbitrary modifications applied. The analysis and visualisation of gear shaper cutters profiles cut by these cutters is impossible without a clear understanding of the principles of operation of gear generation hardware, generating motion and different machine geometry, all being covered in this thesis. In order to verify the analytical model and the CAD software case studies of the real-life gear shaper cutter designs were performed